Problem statement: Segmentation of 3D range images is widely used in computer vision as an essential pre-processing step before the methods of high-level vision can be applied. Segmentation aims to study and recognize the features of range image such as 3D edges, connected surfaces and smooth regions. Approach: This study presents new improvements in segmentation of terrestrial 3D range images based on edge detection technique. The main idea is to apply a gradient edge detector in three different directions of the 3D range images. This 3D gradient detector is a generalization of the classical sobel operator used with 2D images, which is based on the differences of normal vectors or geometric locations in the coordinate directions. The proposed algorithm uses a 3D-grid structure method to handle large amount of unordered sets of points and determine neighborhood points. It segments the 3D range images directly using gradient edge detectors without any further computations like mesh generation. Our algorithm focuses on extracting important linear structures such as doors, stairs and windows from terrestrial 3D range images these structures are common in indoors and outdoors in many environments. Results: Experimental results showed that the proposed algorithm provides a new approach of 3D range image segmentation with the characteristics of low computational complexity and less sensitivity to noise. The algorithm is validated using seven artificially generated datasets and two real world datasets. Conclusion/Recommendations: Experimental results showed that different segmentation accuracy is achieved by using higher Grid resolution and adaptive threshold.
Manual selection of features from massive unstructured point cloud data is a very time-consuming task that requires a considerable amount of human intervention. This work is motivated by the need of fast and simple algorithm to obtain robust, stable and well-localized interest points that are used for subsequent processing in computer vision real-time applications. This paper presents an algorithm for detection of interest points in three-dimensional (3D) point cloud data by using a combined 3D Sobel-Harris operator. The proposed algorithm is compared with six state-of-the-art approaches used to identify the true feature points. Extensive experiments were carried out using synthetic benchmark and real datasets. The datasets were selected with di®erent sizes, features and scales. The results were evaluated against human generated ground truth and prede¯ned feature points. Three measures were used to evaluate the algorithm accuracy, namely localization accuracy L e , False Positive Error (FPE) and False Negative Errors (FNE). Also, the complexity analysis of the proposed algorithm is presented. The results show that the proposed algorithm can identify the interest points with accepted accuracy. It works directly on point cloud datasets and shows superiority when compared with other methods work on 3D mesh data.
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